

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__snake_case				:      Any   		=			{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Dict   		=			[
	    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GraphormerForGraphClassification""",
	    """GraphormerModel""",
	    """GraphormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_graphormer import (
		    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GraphormerForGraphClassification,
		    GraphormerModel,
		    GraphormerPreTrainedModel,
		)


else:
	import sys

	__snake_case				:      int   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


